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Tagged with bayesian-deep-learning bayesian-neural-networks
13 questions
0
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Predicting Values with Bayesian Neural Network
I want to use a Bayesian Neural Network for a regression task.
To do that I converted a BNN from this paper to Python 3. The provided training script runs and I receive a pickle file, which I want to ...
CommunityBot
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2
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1
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436
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How could Bayesian neural networks be used for transfer learning?
In transfer learning, we use big data from similar tasks to learn the parameters of a neural network, and then fine-tune the neural network on our own task that has little data available for it. Here, ...
CommunityBot
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Should we sum or mean reduce the KL loss in Bayes by Backprop?
I'm unsure if you're supposed to use sum or mean reduction of KL loss for Bayes by Backprop. For example, the BayesianTorch library does both: it reduces by mean across each individual tensor (as seen ...
- 41.4k
1
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2
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KL loss not changing in a Bayesian Neural Network?
I've been trying to train a Bayesian Neural Network and I noticed that the KL loss (which enforces the prior) isn't changing over time. And it occurred to me that while in standard Bayesian inference ...
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1
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What is the difference between an input and observed data in a Bayesian neural network?
I'm new to the Bayesian perspective and would appreciate clarity on this.
In a few resources concerning Bayesian deep learning (such as this one), I see this notation:
$p(y|x, D) = \int p(y|x, \theta)...
- 2,632
2
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0
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153
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Today's Practicality of Bayesian Neural Networks
Just having heard lately about BNNs (wow, ANNs and CNNs are clear; now there's a B? What's that? Ahh, Bayesian ;-)) and quickly getting their main idea and focus, that is, weights not being pure ...
- 41.4k
5
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1
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What is the intuition behind variational inference for Bayesian neural networks?
I'm trying to understand the concept of Variational Inference for BNNs. My source is this work. The aim is to minimize the divergence between the approx. distribution and the true posterior
$$\text{KL}...
- 41.4k
1
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1
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What's the likelihood in Bayesian Neural Networks?
I'm trying to understand the concept behind BNN.
Their are based on the Bayes Theorem:
$$p(w \mid \text{data}) = \frac{p(\text{data} \mid w)*p(w)}{p(\text{data})}$$
which boils down to
$$\text{...
3
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Why does this formula $\sigma^2 + \frac{1}{T}\sum_{t=1}^Tf^{\hat{W_t}}(x)^Tf^{\hat{W_t}}(x_t)-E(y)^TE(y)$ approximate the variance?
How does:
$$\text{Var}(y) \approx \sigma^2 + \frac{1}{T}\sum_{t=1}^Tf^{\hat{W_t}}(x)^Tf^{\hat{W_t}}(x_t)-E(y)^TE(y)$$
approximate variance?
I'm currently reading What Uncertainties Do We Need in ...
- 805
4
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1
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Is there any research on models that provide uncertainty estimation?
Is there any research on machine learning models that provide uncertainty estimation?
If I train a denoising autoencoder on words and put through a noised word, I'd like it to return a certainty that ...
- 41.4k
7
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1
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How should the neural network deal with unexpected inputs?
I recently wrote an application using a deep learning model designed to classify inputs. There are plenty of examples of this using images of irises, cats, and other objects.
If I trained a data ...
- 41.4k
2
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1
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1k
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Why is neural networks being a deterministic mapping not always considered a good thing?
Why is neural networks being a deterministic mapping not always considered a good thing?
So I'm excluding models like VAEs since those aren't entirely deterministic. I keep thinking about this and my ...
- 41.4k
2
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Are bayesian neural networks suited for text (or document) classification?
I've tried to do my research on Bayesian neural networks online, but I find most of them are used for image classification. This is probably due to the nature of Bayesian neural networks, which may be ...
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